Data Science is often where things happen when you hear about artificial intelligence or machine learning. But what is it really?
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining. Wikipedia.
Beyond a scholar definition, real life data science is as much of a science as it is an art. It is a science when it comes to methodologies and tools that are put into use. It is the application of the scientific method to the data that surrounds us. The toolbox includes hypothesis testing, statistics, algorithms, informatics and many more. Unfortunately (or not), using tools without purpose, is good for training but barely anything more. The art comes into play when you use data science to answer real life problems. To solve such issues you need to find the connections between them and a large theoretical framework.
One of the core characteristics of Data Science is that it is intrinsically interdisciplinary. In order to extract meaningful business insights, data scientists need to get across usual boundaries. They need to remove corporate silos and make departments talk to each other. This is when data scientists come to handle also project management, politics, communication and human resources. At this point, some might face disillusions that it is not only about cool and complex algorithms, but also about soft skills.
Data scientist is said to be the “21st century sexiest job”, but it is a job which means it has its perks. Hours spent on goal definition and data wrangling and a few moments on modelling. At the end of the day, the reward of bringing added value to the world and making abstractions (data, code, math, …) and people working together harmoniously is what makes Data Science so compelling to me.